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Exploración de la salud mental y el estrés en internos de enfermería a través de un chatbot: un estudio piloto de
Ya-Wen Kuo1, Wen-Li Hou, Susan Fetzer
1Author Affiliations: Professor (Kuo), Undergraduate Nursing Student (Lin), Postdoctoral Research Fellow (Hsu), Assistant Professor (Liu), Department of Nursing, Chang Gung University of Science and Technology, Chiayi Campus, Puzi, Chiayi County, Taiwan; Associate Professor (Hou), Department of Nursing, Kaohsiung Medical University, Kaohsiung, Taiwan; Professor (Fetzer), Department of Nursing, College of Health and Human Services, University of New Hampshire, Durham, New Hampshire; Professor (Kuo), Attending Neurologist (Lee), Department of Neurology, Chang Gung Memorial Hospital, Chiayi Branch, Chiayi, Taiwan; Physician and Associate Professor (Lee), Department of Medicine, Chang Gung University, Taoyuan, Taiwan.
Background:
Nursing interns face early clinical distress; chatbot-based mental health tools show promise, although evidence of their feasibility and educational value remains insufficient.
Purpose:
To evaluate the application feasibility and effectiveness of the Xiao Ling Assistant Chatbot (X-LAC) for addressing mental health challenges, monitoring stress, and detecting early warning signals of psychological distress among nursing students participating in a clinical internship.
Methods:
A 4-week single-group study with 61 nursing interns tested X-LAC's daily chatbot-based support and keyword-triggered alerts; pre/post mental health and stress were assessed.
Results:
Well-being improved (12-20); suicidal ideation declined (10-4). The chatbot flagged 16 high-risk expressions per 100 messages, notably so tired and under pressure; 60.6% reported internship distress.
Conclusion:
Chatbot support shows promise for clinical training, reducing stress and enabling early detection. Integrating artificial intelligence for risk prediction is warranted while retaining human oversight for critical cases.
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